What Does the Public Think? Examining Plastic Surgery Perceptions through the Twitterverse
Bibliographic record
Abstract
BACKGROUND: Twitter is a recognized social media platform for communication of health information. Rime reported that emotion is the main motive for social sharing. This study is a content analysis of Twitter that was performed to identify the public's perceptions and attitudes toward plastic surgery and the emotional triggers that drive social sharing of plastic surgery information. METHODS: Tweets containing "#PlasticSurgery" or "Plastic Surgery" were archived randomly from August 1, 2014, to December 30, 2016 (n = 4548). Tweets were categorized according to tweet author, specialty, topic, content, multimedia included, emotion, tone, accuracy of information, source, and retweet rate. Statistical analysis was performed to detect significant patterns. RESULTS: Tweets on cosmetic surgery (74 percent) were shared mostly on Twitter, predominantly posted by the public [n = 1611 (48 percent)]. More than 13 percent of posts contained "celebrity news" and 42.8 percent contained professional information and resources. The most frequent emotions shared and retweeted were "relaxed/content" (51.5 percent) and "excited/interested" (18.4 percent). Most tweets posted by the public contained inaccurate information [n = 1486 (80 percent)]. Only 154 (11.2 percent) of board-certified plastic surgeons' tweets were rated as "most accurate." CONCLUSIONS: The majority of tweets posted on Twitter contained inaccurate information that can lead to misperception among the public. Understanding emotional triggers for social sharing provides insight into what is most appealing. To enhance public uptake and sharing of tweets, plastic surgeons can use these findings to promote the specialty using relaxed/content emotions or excitement in their social media posts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".